Artificial intelligence and discovery of microbial natural products

Natural products (NPs) have a variety of potential medicinal applications as drug or lead molecules for drug development. NPs are subset of small chemical molecules known as secondary metabolites that are produced by living organisms, including microorganisms. Artificial intelligence (AI) is the fourth industrial revolution that significantly reduces the cost and time of drug discovery. Over the past 10 years, the tremendous advancement in AI and its applications has improved the NPs discovery through the extensive analysis of large collected computerized, experimental genomics, transcriptomics, and metabolomics data. By using machine learning (ML) algorithms, AI can identify patterns and relationships within the data that are difficult or impossible to discern through manual analysis. AI can be employed in the discovery and elucidation of NPs' chemical structure, mechanism of action, toxicity, and phenotypic activity. AI can also assist in the design of experiments to screen for new microbial metabolites. By using optimization algorithms, AI can identify the most promising conditions for screening experiments based on a variety of factors, such as the characteristics of the microorganism, the type of metabolite being produced, and the growth condition. AI can aid in mining the microbial genome for the discovery and development of novel bioactive microbial metabolite. AI can assess a train model for virtual screening of NPs' chemical space database for biological active lead and subsequent inspiration of de novo design of NPs mimetics compounds with improved attributes. Taken together, AI has the potential to transform the future of NPs discovery and development.